Microtubule Tracking in Electron Microscopy Volumes

• Main Authors: Eckstein, Nils, Buhmann, Julia, Cook, Matthew, Funke, Jan
• Format: Journal Article
• Language: English
• Published: 17-09-2020
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Quantitative Biology - Quantitative Methods
• DOI: 10.48550/arxiv.2009.08371

We present a method for microtubule tracking in electron microscopy volumes.
Our method first identifies a sparse set of voxels that likely belong to
microtubules. Similar to prior work, we then enumerate potential edges between
these voxels, which we represent in a candidate graph. Tracks of microtubules
are found by selecting nodes and edges in the candidate graph by solving a
constrained optimization problem incorporating biological priors on microtubule
structure. For this, we present a novel integer linear programming formulation,
which results in speed-ups of three orders of magnitude and an increase of 53%
in accuracy compared to prior art (evaluated on three 1.2 x 4 x 4$\mu$m volumes
of Drosophila neural tissue). We also propose a scheme to solve the
optimization problem in a block-wise fashion, which allows distributed tracking
and is necessary to process very large electron microscopy volumes. Finally, we
release a benchmark dataset for microtubule tracking, here used for training,
testing and validation, consisting of eight 30 x 1000 x 1000 voxel blocks (1.2
x 4 x 4$\mu$m) of densely annotated microtubules in the CREMI data set
(https://github.com/nilsec/micron).